The article delves into various local model-agnostic methods used to interpret machine learning models, emphasizing the significance of understanding individual predictions rather than the entire model. It introduces Local Interpretable Model-agnostic Explanations (LIME), which approximates individual predictions by perturbing data points and observing the results. The Individual Conditional Expectation (ICE) is compared with Partial Dependence Plot (PDP) to highlight its focus on individual instances instead of average effects, offering insights into how predictions change with feature variations. Additionally, the concept of Shapley Values from game theory is discussed as a method to fairly attribute the contribution of each feature to the model's outcome, with SHAP being an extension that combines LIME and Shapley Values for more comprehensive interpretation. The article uses practical examples, such as a bike-sharing dataset and a cervical cancer dataset, to illustrate these methods, highlighting their respective advantages and limitations.